Case Study Telecom Churn Analysis

• State: US state in which the customer resides

• Account length: The number of days that this account has been active

• Area code: The three-digit area code of the corresponding customer’s phone number

• International plan: Customer has an international calling plan: YES / NO

• Voice mail plan: Customer has a voice mail feature: YES / NO

• Number vmail messages: average number of voice mail messages per month

• Total day minutes: total number of calling minutes used during the day

• Total day calls: total number of calls placed during the day

• Total day charge: billed cost of daytime calls

• Total eve minutes: total number of calling minutes used during the evening

• Total eve calls: total number of calls placed during the evening

• Total eve charge: billed cost of evening time calls

• Total night minutes: total number of calling minutes used during the night

• Total night calls: the total number of calls placed during the night

• Total night charge: the billed cost of nighttime calls

• Total intl minutes: total number of international minutes

• Total intl calls: total number of international calls

• Total intl charge: billed cost for international calls

• Customer service calls: number of calls placed to Customer Service

• Churn: whether the customer left the service: True/False

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import pandas_profiling
import warnings
warnings.filterwarnings('ignore')
plt.rcParams["figure.figsize"]=(10,6)

Importing Dataset

df=pd.read_csv('Telecom_Churn_Analysis.csv')
df.head()

	State	Account length	Area code	International plan	Voice mail plan	Number vmail messages	Total day minutes	Total day calls	Total day charge	Total eve minutes	Total eve calls	Total eve charge	Total night minutes	Total night calls	Total night charge	Total intl minutes	Total intl calls	Total intl charge	Customer service calls	Churn
0	KS	128	415	No	Yes	25	265.1	110	45.07	197.4	99	16.78	244.7	91	11.01	10.0	3	2.70	1	False
1	OH	107	415	No	Yes	26	161.6	123	27.47	195.5	103	16.62	254.4	103	11.45	13.7	3	3.70	1	False
2	NJ	137	415	No	No	0	243.4	114	41.38	121.2	110	10.30	162.6	104	7.32	12.2	5	3.29	0	False
3	OH	84	408	Yes	No	0	299.4	71	50.90	61.9	88	5.26	196.9	89	8.86	6.6	7	1.78	2	False
4	OK	75	415	Yes	No	0	166.7	113	28.34	148.3	122	12.61	186.9	121	8.41	10.1	3	2.73	3	False

Profile Report of dataset

# df.profile_report(title='Profile Report')

Check basic information of dataset

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2666 entries, 0 to 2665
Data columns (total 20 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   State                   2666 non-null   object 
 1   Account length          2666 non-null   int64  
 2   Area code               2666 non-null   int64  
 3   International plan      2666 non-null   object 
 4   Voice mail plan         2666 non-null   object 
 5   Number vmail messages   2666 non-null   int64  
 6   Total day minutes       2666 non-null   float64
 7   Total day calls         2666 non-null   int64  
 8   Total day charge        2666 non-null   float64
 9   Total eve minutes       2666 non-null   float64
 10  Total eve calls         2666 non-null   int64  
 11  Total eve charge        2666 non-null   float64
 12  Total night minutes     2666 non-null   float64
 13  Total night calls       2666 non-null   int64  
 14  Total night charge      2666 non-null   float64
 15  Total intl minutes      2666 non-null   float64
 16  Total intl calls        2666 non-null   int64  
 17  Total intl charge       2666 non-null   float64
 18  Customer service calls  2666 non-null   int64  
 19  Churn                   2666 non-null   bool   
dtypes: bool(1), float64(8), int64(8), object(3)
memory usage: 398.5+ KB

df['Churn'].mean()

0.145536384096024

14% Customer Left the service

Describe Dataset

df.describe()

	Account length	Area code	Number vmail messages	Total day minutes	Total day calls	Total day charge	Total eve minutes	Total eve calls	Total eve charge	Total night minutes	Total night calls	Total night charge	Total intl minutes	Total intl calls	Total intl charge	Customer service calls
count	2666.000000	2666.000000	2666.000000	2666.00000	2666.000000	2666.000000	2666.000000	2666.000000	2666.000000	2666.000000	2666.000000	2666.000000	2666.000000	2666.000000	2666.000000	2666.000000
mean	100.620405	437.438860	8.021755	179.48162	100.310203	30.512404	200.386159	100.023631	17.033072	201.168942	100.106152	9.052689	10.237022	4.467367	2.764490	1.562641
std	39.563974	42.521018	13.612277	54.21035	19.988162	9.215733	50.951515	20.161445	4.330864	50.780323	19.418459	2.285120	2.788349	2.456195	0.752812	1.311236
min	1.000000	408.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	43.700000	33.000000	1.970000	0.000000	0.000000	0.000000	0.000000
25%	73.000000	408.000000	0.000000	143.40000	87.000000	24.380000	165.300000	87.000000	14.050000	166.925000	87.000000	7.512500	8.500000	3.000000	2.300000	1.000000
50%	100.000000	415.000000	0.000000	179.95000	101.000000	30.590000	200.900000	100.000000	17.080000	201.150000	100.000000	9.050000	10.200000	4.000000	2.750000	1.000000
75%	127.000000	510.000000	19.000000	215.90000	114.000000	36.700000	235.100000	114.000000	19.980000	236.475000	113.000000	10.640000	12.100000	6.000000	3.270000	2.000000
max	243.000000	510.000000	50.000000	350.80000	160.000000	59.640000	363.700000	170.000000	30.910000	395.000000	166.000000	17.770000	20.000000	20.000000	5.400000	9.000000

Check Unique Value in dataset

df['State'].unique()

array(['KS', 'OH', 'NJ', 'OK', 'AL', 'MA', 'MO', 'WV', 'RI', 'IA', 'MT',
       'ID', 'VT', 'VA', 'TX', 'FL', 'CO', 'AZ', 'NE', 'WY', 'IL', 'NH',
       'LA', 'GA', 'AK', 'MD', 'AR', 'WI', 'OR', 'DE', 'IN', 'UT', 'CA',
       'SD', 'NC', 'WA', 'MN', 'NM', 'NV', 'DC', 'NY', 'KY', 'ME', 'MS',
       'MI', 'SC', 'TN', 'PA', 'HI', 'ND', 'CT'], dtype=object)

df['International plan'].unique()

array(['No', 'Yes'], dtype=object)

df['Voice mail plan'].unique()

array(['Yes', 'No'], dtype=object)

Cleaning

Check the count of Null values in data set

df.isna().sum()

State                     0
Account length            0
Area code                 0
International plan        0
Voice mail plan           0
Number vmail messages     0
Total day minutes         0
Total day calls           0
Total day charge          0
Total eve minutes         0
Total eve calls           0
Total eve charge          0
Total night minutes       0
Total night calls         0
Total night charge        0
Total intl minutes        0
Total intl calls          0
Total intl charge         0
Customer service calls    0
Churn                     0
dtype: int64

There is no Null Value in Dataset

Churn Column Replace Bool Value to 0 & 1

df['Churn']=df['Churn'].replace({False:0, True:1})
df.head()

	State	Account length	Area code	International plan	Voice mail plan	Number vmail messages	Total day minutes	Total day calls	Total day charge	Total eve minutes	Total eve calls	Total eve charge	Total night minutes	Total night calls	Total night charge	Total intl minutes	Total intl calls	Total intl charge	Customer service calls	Churn
0	KS	128	415	No	Yes	25	265.1	110	45.07	197.4	99	16.78	244.7	91	11.01	10.0	3	2.70	1	0
1	OH	107	415	No	Yes	26	161.6	123	27.47	195.5	103	16.62	254.4	103	11.45	13.7	3	3.70	1	0
2	NJ	137	415	No	No	0	243.4	114	41.38	121.2	110	10.30	162.6	104	7.32	12.2	5	3.29	0	0
3	OH	84	408	Yes	No	0	299.4	71	50.90	61.9	88	5.26	196.9	89	8.86	6.6	7	1.78	2	0
4	OK	75	415	Yes	No	0	166.7	113	28.34	148.3	122	12.61	186.9	121	8.41	10.1	3	2.73	3	0

EDA

Display Histogram of All numeric columns

df.hist(figsize=(20,20), grid=False, color='lightgreen');

Hear from Histo Grram, say that Account length, Total day minutes, Total day calls, Total day charge, Total eve minutes, Total eve calls, Total eve charge, Total night minutes, Total night calls, Total night charge, Total intl minutes, Total intl charge Columns are Normal Distribution

Check the churn column value count

df['Churn'].value_counts()

0    2278
1     388
Name: Churn, dtype: int64

Display count plot of Churn

sns.countplot(data=df, x='Churn')

<AxesSubplot:xlabel='Churn', ylabel='count'>

# sns.pairplot(data=df)

Check the International plan column value count

df['International plan'].value_counts()

No     2396
Yes     270
Name: International plan, dtype: int64

Check the Voice mail plan column value count

df['Voice mail plan'].value_counts()

No     1933
Yes     733
Name: Voice mail plan, dtype: int64

Display the Pie Plot of International plan & Voice mail plan columns

plt.subplot(1,2,1)
plt.title('◈International plan count')
df['International plan'].value_counts().plot(kind='pie', cmap='Accent', autopct=lambda p : '{:.0f}'.format(p * (df['International plan'].count()) / 100), wedgeprops=dict(width=.25), shadow=True)


plt.subplot(1,2,2)
plt.title('◈Voice mail plan count')
df['Voice mail plan'].value_counts().plot(kind='pie', autopct=lambda p : '{:.0f}'.format(p * (df['Voice mail plan'].count()) / 100), cmap='Set2',wedgeprops=dict(width=.25), shadow=True)

<AxesSubplot:title={'center':'◈Voice mail plan count'}, ylabel='Voice mail plan'>

Display the International plan & Voice mail plan count respect to Churn

plt.subplot(1,2,1)
plt.title('◈ International plan count respect to Churn',)
df.groupby('International plan')['Churn'].value_counts().plot(kind='pie', autopct=lambda p : '{:.0f}'.format(p * (df['International plan'].count()) / 100), cmap='Set3_r',wedgeprops=dict(width=.25), shadow=True);

plt.subplot(1,2,2)
plt.title('◈ Voice mail plan count respect to Churn')
df.groupby('Voice mail plan')['Churn'].value_counts().plot(kind='pie' ,autopct=lambda p : '{:.0f}'.format(p * (df['Voice mail plan'].count()) / 100), cmap='Set3_r',wedgeprops=dict(width=.25), shadow=True);

plt.subplot(1,2,1)
sns.barplot(data=df, x='International plan', y='Churn', color='darksalmon')

plt.subplot(1,2,2)
sns.barplot(data=df, x='Voice mail plan', y='Churn', color='wheat')

plt.show()

sns.lmplot(x='Total day charge', y='Total night charge', data=df, hue='Churn',markers=['*','o'],size=8,fit_reg=False);

Finds correlation between Independent and dependent attributes

plt.figure(figsize=(30,30))
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
cmap = sns.light_palette('#3fdd01',as_cmap=True)
sns.heatmap(df.corr(),annot=True, cmap=cmap, linewidths=2, linecolor='red')

<AxesSubplot:>

sns.pairplot(df, hue='Churn')

<seaborn.axisgrid.PairGrid at 0x1bc1ee7c820>

sns.countplot(x='Customer service calls', data=df, palette="pastel")

<AxesSubplot:xlabel='Customer service calls', ylabel='count'>

sns.barplot(data=df, x='Churn', y='Total day minutes', color='pink')

<AxesSubplot:xlabel='Churn', ylabel='Total day minutes'>

sns.barplot(data=df, x='Area code', y='Customer service calls', hue='Churn')

<AxesSubplot:xlabel='Area code', ylabel='Customer service calls'>

sns.boxplot(x='Churn', y='Total day charge', data=df)

<AxesSubplot:xlabel='Churn', ylabel='Total day charge'>

We can go for Machine Learning

df.head()

	State	Account length	Area code	International plan	Voice mail plan	Number vmail messages	Total day minutes	Total day calls	Total day charge	Total eve minutes	Total eve calls	Total eve charge	Total night minutes	Total night calls	Total night charge	Total intl minutes	Total intl calls	Total intl charge	Customer service calls	Churn
0	KS	128	415	No	Yes	25	265.1	110	45.07	197.4	99	16.78	244.7	91	11.01	10.0	3	2.70	1	0
1	OH	107	415	No	Yes	26	161.6	123	27.47	195.5	103	16.62	254.4	103	11.45	13.7	3	3.70	1	0
2	NJ	137	415	No	No	0	243.4	114	41.38	121.2	110	10.30	162.6	104	7.32	12.2	5	3.29	0	0
3	OH	84	408	Yes	No	0	299.4	71	50.90	61.9	88	5.26	196.9	89	8.86	6.6	7	1.78	2	0
4	OK	75	415	Yes	No	0	166.7	113	28.34	148.3	122	12.61	186.9	121	8.41	10.1	3	2.73	3	0

Feature Engineering

Apply Encoding in Categorical columns

One-Hot Encoding

x=df.drop(['Churn'], axis=1)
y=df['Churn']

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

x=pd.get_dummies(x)
x.head()

	Account length	Area code	Number vmail messages	Total day minutes	Total day calls	Total day charge	Total eve minutes	Total eve calls	Total eve charge	Total night minutes	...	State_VA	State_VT	State_WA	State_WI	State_WV	State_WY	International plan_No	International plan_Yes	Voice mail plan_No	Voice mail plan_Yes
0	128	415	25	265.1	110	45.07	197.4	99	16.78	244.7	...	0	0	0	0	0	0	1	0	0	1
1	107	415	26	161.6	123	27.47	195.5	103	16.62	254.4	...	0	0	0	0	0	0	1	0	0	1
2	137	415	0	243.4	114	41.38	121.2	110	10.30	162.6	...	0	0	0	0	0	0	1	0	1	0
3	84	408	0	299.4	71	50.90	61.9	88	5.26	196.9	...	0	0	0	0	0	0	0	1	1	0
4	75	415	0	166.7	113	28.34	148.3	122	12.61	186.9	...	0	0	0	0	0	0	0	1	1	0

5 rows × 71 columns

Splitting the data in training and testing sets

from sklearn.model_selection import train_test_split

 x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=13)

Applying All Algorithms

from sklearn.linear_model import LogisticRegression,SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier,AdaBoostClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB

from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, classification_report

models={'LogisticRegression':LogisticRegression(),
        'SGDC':SGDClassifier(),
        'DecisionTree':DecisionTreeClassifier(),
        'RandomForest':RandomForestClassifier(),
        'KNN':KNeighborsClassifier(),
        'SVM':SVC()
       }

for name, model in models.items():
    model1=model.fit(x_train,y_train)
    y_pred = model1.predict(x_test)
    print(f'{name} accuracy score: {accuracy_score(y_test, y_pred)}')

LogisticRegression accuracy score: 0.8670411985018727
SGDC accuracy score: 0.8670411985018727
DecisionTree accuracy score: 0.9119850187265918
RandomForest accuracy score: 0.9456928838951311
KNN accuracy score: 0.8707865168539326
SVM accuracy score: 0.8670411985018727

RandomForestClassifier which shows an accuracy of nearly 94%

Here we see RandomForestRegressor gives us best score so we can use RandomForest Regressor algorithm

Algorithm selected: RandomForestClassifier

from sklearn.ensemble import RandomForestClassifier
reg_rf = RandomForestClassifier()
reg_rf.fit(x_train, y_train)

RandomForestClassifier()

y_pred = reg_rf.predict(x_test)

reg_rf.score(x_train, y_train)

1.0

reg_rf.score(x_test, y_test)

0.9419475655430711

The next step consists of finding the combination of hyperparameters

Perform Hyper-Prameter tuning using RandomizedSearchCV

from sklearn.model_selection import RandomizedSearchCV

create list for all possible parameter

n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]
max_features = ['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]
min_samples_split = [2, 5, 10, 15, 100]
min_samples_leaf = [1, 2, 5, 10]

n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]

n_estimators

[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]

random_grid = {'n_estimators': n_estimators,
               'max_features': max_features,
               'max_depth': max_depth,
               'min_samples_split': min_samples_split,
               'min_samples_leaf': min_samples_leaf}

rf_random = RandomizedSearchCV(estimator = reg_rf, 
                               param_distributions = random_grid,
                               scoring='neg_mean_squared_error', 
                               n_iter = 10, cv = 5,
                               verbose=2,
                               random_state=42, n_jobs = 1)

rf_random.fit(x_train,y_train)

Fitting 5 folds for each of 10 candidates, totalling 50 fits
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.9s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.9s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.9s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.9s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.8s
[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.6s
[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.6s
[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   3.3s
[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   3.2s
[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   3.3s
[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.6s
[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.6s
[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.6s
[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.5s
[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.6s
[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.9s
[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.9s
[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.8s
[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.9s
[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.9s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.5s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.4s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.4s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.4s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.5s
[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.6s
[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.7s
[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.6s
[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.6s
[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.6s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.7s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.8s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.7s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.8s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.9s
[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.7s
[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.7s
[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.7s
[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.7s
[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.7s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.2s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.4s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.4s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.3s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.8s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.7s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.9s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.7s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.7s

RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=1,
                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],
                                        'max_features': ['auto', 'sqrt'],
                                        'min_samples_leaf': [1, 2, 5, 10],
                                        'min_samples_split': [2, 5, 10, 15,
                                                              100],
                                        'n_estimators': [100, 200, 300, 400,
                                                         500, 600, 700, 800,
                                                         900, 1000, 1100,
                                                         1200]},
                   random_state=42, scoring='neg_mean_squared_error',
                   verbose=2)

rf_random.best_params_

{'n_estimators': 1000,
 'min_samples_split': 2,
 'min_samples_leaf': 1,
 'max_features': 'sqrt',
 'max_depth': 25}

rf_random_pred=rf_random.predict(x_test)

accuracy_score(y_test, rf_random_pred)

0.9419475655430711

Performace of the model

from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, classification_report

print(f'confusion_matrix:- \n {confusion_matrix(y_test,rf_random_pred)}\n')
print(f'classification report:- \n{classification_report(y_test,rf_random_pred)}')

confusion_matrix:- 
 [[461   2]
 [ 29  42]]

classification report:- 
              precision    recall  f1-score   support

           0       0.94      1.00      0.97       463
           1       0.95      0.59      0.73        71

    accuracy                           0.94       534
   macro avg       0.95      0.79      0.85       534
weighted avg       0.94      0.94      0.94       534

import pickle
telecom_model=open('telecon_model.sav','wb')
pickle.dump(rf_random,telecom_model)